Bottom Line:
Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology.The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes.Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets.

ABSTRACTElucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3'-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3'-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.

pcbi-1000189-g005: AU normalization.M-AU plots without (A) and after (B) applying an AU normalization scheme to the technical dataset which profiled the universal reference RNA pool.

Mentions:
As shown, the AU response bias causes many false positive calls in computational search for active miRs from expression data, and therefore its removal is crucial when carrying out integrated bioinformatics analysis of mRNA expression data and 3′-UTR sequences. To remove this bias, we adopted the lowess normalization method which is routinely used to remove intensity biases from microarray data [21], and adjusted it to cancel AU biases (Figure 5) (see Methods). Applying AU normalization did not distort the normalization at the M-A plane (Figure S6). Importantly, after applying AU normalization to the negative control dataset, no miR family passed the statistical significance threshold (0.0003, which corresponds to 0.05 after Bonferroni correction for multiple testing) (Table 1).

pcbi-1000189-g005: AU normalization.M-AU plots without (A) and after (B) applying an AU normalization scheme to the technical dataset which profiled the universal reference RNA pool.

Mentions:
As shown, the AU response bias causes many false positive calls in computational search for active miRs from expression data, and therefore its removal is crucial when carrying out integrated bioinformatics analysis of mRNA expression data and 3′-UTR sequences. To remove this bias, we adopted the lowess normalization method which is routinely used to remove intensity biases from microarray data [21], and adjusted it to cancel AU biases (Figure 5) (see Methods). Applying AU normalization did not distort the normalization at the M-A plane (Figure S6). Importantly, after applying AU normalization to the negative control dataset, no miR family passed the statistical significance threshold (0.0003, which corresponds to 0.05 after Bonferroni correction for multiple testing) (Table 1).

Bottom Line:
Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology.The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes.Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets.

ABSTRACTElucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3'-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3'-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.